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Received：30 September 2018，
Revised：2019-3-22，
Published：16 September 2019
移动端阅览
目的
2
建立行为可信的虚拟角色能够使严肃游戏更加有趣，提升用户使用的体验感。尽管严肃游戏的图形渲染技术已经日趋成熟，但现有的虚拟角色行为表现方式多采用确定性模型，很难反映虚拟角色行为表现的多样性。
方法
2
本文构建了符合辅助社交训练的严肃游戏剧情，采用智能体来描述虚拟角色，赋予虚拟角色视觉、听觉双通道感知。基于马斯洛动机理论，采用食物、休息、交流和安全等动机来描述情绪的产生，利用大五（OCEAN）个性模型来描述虚拟角色的不同个性差别。用外部刺激和内部动机需求来计算情绪强度，利用行为树描述虚拟角色的行为。运用正态云模型处理虚拟角色行为表现的不确定性，并以行走方向、社交距离、交流时身体朝向3个典型的行为表现给出了具体处理方法。
结果
2
在所实现的游戏原型系统中，对于虚拟角色的自主行为和行为表现的不确定性进行了用户体验测试。结果表明，在场景探索任务中，虚拟角色的自主行为模型能减少用户探索场景所耗费的时间，并且可以促进用户与虚拟角色交流；在行为表现测试中，本文模型的自然性评价要高于确定性模型。
结论
2
本文虚拟角色行为模型在一定程度上可提升用户的体验感，有望为建立行为可信的虚拟角色提供一种新的途径。
Objective
2
Virtual characters with believable behavior can make serious games more interesting and enhance users' experiences. Although the graphics rendering technology of serious games has become more and more mature
most existing virtual character behavioral models are based on deterministic models
which are difficult to reflect the diversity of virtual characters' behaviors. However
humans' behaviors are generally uncertain. On the one hand
the variables involved in behavior generation are ambiguous; on the other hand
behavioral performance
which is mostly realized through body movements
expressions
and interpersonal interactions
is random. The cloud model
a method proposed by DeyiLi to deal with uncertain information
can be a solution to this problem. The model has been applied in many areas
such as pattern recognition
but its application in serious games has not been reported.
Method
2
In the proposed game
the plot is designed according to the needs of social training
and agents are used to describe virtual characters. The proposed framework will generate autonomous behavior includes three layers:sensing layer
decision layer
and action layer. The sensing layer acquires external environment information (including stimuli
events
and other virtual characters in the virtual environment) through visual and auditory channels. Each virtual character has a perceptible area. If an object enters into the perceptible area
it will be perceived by the virtual character. The acquired information will then be stored in the memory of the sensing layer and transferred to the database
which stores a virtual character's identity
personality
initial location information
and animation data. The database information will be updated over time. The decision layer is composed of a motivation module
a behavior module
and an emotion module. The motivation module generates motivation. When the motivation intensity reaches a certain value
it triggers the corresponding behavior and emotional state. Based on Maslow's motivation theory
the motivations of finding food
taking rest
doing communication and keeping safety are used to describe the generation of emotions. The big five (OCEAN) personality model is used to divide the virtual characters into five categories:openness
conscientiousness
extraversion
agreeableness
and neuroticism. The behavior module generates behavior
and the emotion module generates the emotion for the virtual character on the basis of the relevant information transmitted by the sensing layer. The intensity of the emotion is calculated according to the distance from the stimulus and the strength of the current motivation. It determines the intensity of the emotional performance and behavioral performance of the virtual character. The action layer includes a navigation module and an action module
which acquires characters' animation data from the database and renders skeletal animation. The navigation module plans the path according to the final destination selected in the decision layer. It also detects possible collisions that may occur between the virtual character and other virtual characters or obstacles in the environment. The behavioral trees are used to describe virtual characters' behaviors. The normal cloud model is used to deal with the uncertainty of the behavior of virtual characters
and specific design methods are provided for the three typical behaviors during communication
walking direction
social distance
and body orientation.
Result
2
In the developed game prototype system
the user experience test is carried out to assess the uncertainty of virtual characters' autonomous behavior and behavioral performance. Five children and eleven adults participants are recruited to test the useful of the proposed behavioral models. The 16 participants are divided equally into two groups:experimental group and control group. Participants in experimental group plays the game with autonomous behavioral model while participants in control group plays the game with script-driven behavioral model. Test results showed that the autonomous behavioral model can reduce the time of exploring the scene
and can promote the user to communicate with the virtual character; To test of the uncertainty of behavior performance
thirteen volunteers are recruited. Game video clips are used to compare the changes in walking direction
social distance
and body orientation. The upper part of the contrasted video is the deterministic model
and the lower part is the uncertainty. Volunteers who are not in turn cannot watch other's operation processes to ensure the authenticity of the experimental results. The naturalness of the three behavior is scored.
Conclusion
2
Results showed that the proposed model can generate more natural behavior than the deterministic model and obtain higher recognition from users. The virtual characters under the proposed model are natural and attractive to users
and the method can enhance user experience.
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